Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models
| Autor: | Kaan Öcal, Michael U. Gutmann, Guido Sanguinetti, Ramon Grima |
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| Rok vydání: | 2022 |
| Předmět: |
Stochastic Processes
uncertainty quantification Bayesian inference Uncertainty Biomedical Engineering Biophysics Gene Expression Chemical Master Equation Bioengineering Models Biological Biochemistry Settore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin) Synthetic Likelihoods Stochastic Modelling Biomaterials chemical master equation Bayesian Inference Uncertainty Quantification synthetic likelihoods stochastic modelling Algorithms Biotechnology |
| Zdroj: | Öcal, K, Gutmann, M U, Sanguinetti, G & Grima, R 2022, ' Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models ', Journal of the Royal Society. Interface, vol. 19, no. 192, 20220153 . https://doi.org/10.1098/rsif.2022.0153 |
| DOI: | 10.1101/2022.01.25.477666 |
| Popis: | Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of nontrivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression. |
| Databáze: | OpenAIRE |
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